Pandas is an amazing data analysis toolkit for Python. It is designed to operate on relational or labeled data and gives you tools to slice and dice as you please.
In this two-part tutorial, you’ll learn about the fundamental data structures of Pandas: the series and the data frame. You’ll also learn how to select data, deal with missing values, manipulate your data, merge your data, group your data, work with time series, and even plot data.
Installation
To install, just pip install pandas. It will take care of installing numpy too if you don’t have it installed.
Series
Pandas series are typed and labeled 1-D arrays. This means that each element can be accessed by its label in addition to its index.
Here is a series of integers where the labels are Roman numerals. You can index and slice using the labels or integer indices. Unlike with regular Python list slicing, when using labels the last item is included!
>>> s = pd.Series(np.arange(1,5), ['I', 'II', 'III', 'IV', 'V']) >>> s['III'] 3 >>> s[0] 1 >>> s['II':'V'] II 2 III 3 IV 4 V 5 >>> s[1:5] II 2 III 3 IV 4 V 5
If you don’t provide an index then a 0-based integer index is automatically created:
>>> s = pd.Series((50, 7, 88, 9)) >>> s 0 50 1 7 2 88 3 9
Now, here is a little secret for you. Pandas series are a wrapper around Numpy’s arrays.
>>> s.values array([50, 7, 88, 9]) >>> type(s.values)
Unlike Python lists or numpy arrays, operations on series align on the index. If the indexes don’t match then the union of indices will be used with missing values as appropriate. Here are a few examples using dicts as data so the keys become the series index:
>>> s1 = pd.Series(dict(a=1, b=2, c=3)) >>> s2 = pd.Series(dict(a=4, b=5, c=6, d=7)) >>> s1 + s2 a 5.0 b 7.0 c 9.0 d NaN >>> s1[1:] * s2[:-1] a NaN b 10.0 c 18.0
Data Frames
Data frames are the primary pandas data structure. They represent tables of data where each column is a series. Data frames have an index too, which serves as a row label. A data frame also has column labels. Here is how to declare a data frame using a dict.
>>> df = pd.DataFrame(dict(a=[1, 2, 3],
b=[4,5,6],
c=pd.Timestamp('20170902'),
d=pd.Categorical(['red',
'green',
'blue'])))
>>> df
a b c d
0 1 4 2017-09-02 red
1 2 5 2017-09-02 green
2 3 6 2017-09-02 blue
Note that an integer index (row label) was created automatically. You can of course provide your own index:
>>> df.index = ('I II III'.split())
>>> df
a b c d
I 1 4 2017-09-02 red
II 2 5 2017-09-02 green
III 3 6 2017-09-02 blue
Importing and Exporting Data
Data frames can be constructed from a very wide variety of sources:
- dict of 1-D ndarrays, lists, dicts, or series
- 2-D numpy.ndarray
- structured or record ndarray
- another DataFrame
You can also import or load data from many file formats and databases such as:
- CSV
- Excel
- HTML
- HDFStore
- SQL
Here is how to read a CSV file:
data.csv
--------
I,1,4,2017-09-02,red
II,2,5,2017-09-02,green
III,3,6,2017-09-02,blue
>>> pd.read_csv('data.csv')
I 1 4 2017-09-02 red
0 II 2 5 2017-09-02 green
1 III 3 6 2017-09-02 blue
Here is the complete list of read_functions():
>>> read_functions = [a for a in dir(pd) if a.startswith('read_')]
>>> print('n'.join(read_functions))
read_clipboard
read_csv
read_excel
read_feather
read_fwf
read_gbq
read_hdf
read_html
read_json
read_msgpack
read_pickle
read_sas
read_sql
read_sql_query
read_sql_table
read_stata
read_table
There are corresponding methods on the data frame object itself for exporting the data to many formats and databases. Here is how you export to json and msgpack:
>>> df.to_json()
'{"a":{"I":1,"II":2,"III":3},
"b":{"I":4,"II":5,"III":6},
"c":{"I":1504310400000,"II":1504310400000,"III":1504310400000},
"d":{"I":"red","II":"green","III":"blue"}}'
>>> df.to_msgpack()
b'x84xa3typxadblock_managerxa5klassxa9DataFramexa4axes
x92x86xa3typxa5indexxa5klassxa5Indexxa4namexc0xa5dtype
xa6objectxa4datax94xa1axa1bxa1cxa1dxa8compressxc0x86
xa3typxa5indexxa5klassxa5Indexxa4namexc0xa5dtype
xa6objectxa4datax93xa1Ixa2IIxa3IIIxa8compressxc0
xa6blocksx93x86xa4locsx86xa3typxa7ndarrayxa5shapex91
x02xa4ndimx01xa5dtypexa5int64xa4dataxd8x00x00x00x00
x00x00x00x00x00x01x00x00x00x00x00x00x00xa8compress
xc0xa6valuesxc70x00x01x00x00x00x00x00x00x00x02x00
x00x00x00x00x00x00x03x00x00x00x00x00x00x00x04
x00x00x00x00x00x00x00x05x00x00x00x00x00x00x00
x06x00x00x00x00x00x00x00xa5shapex92x02x03xa5dtype
xa5int64xa5klassxa8IntBlockxa8compressxc0x86xa4locsx86
xa3typxa7ndarrayxa5shapex91x01xa4ndimx01xa5dtype
xa5int64xa4dataxd7x00x02x00x00x00x00x00x00x00
xa8compressxc0xa6valuesxc7x18x00x00x00xedxafVbxe0
x14x00x00xedxafVbxe0x14x00x00xedxafVbxe0x14
xa5shapex92x01x03xa5dtypexaedatetime64[ns]xa5klass
xadDatetimeBlockxa8compressxc0x86xa4locsx86xa3typ
xa7ndarrayxa5shapex91x01xa4ndimx01xa5dtypexa5int64
xa4dataxd7x00x03x00x00x00x00x00x00x00xa8compress
xc0xa6valuesx87xa3typxa8categoryxa5klassxabCategorical
xa4namexc0xa5codesx86xa3typxa7ndarrayxa5shapex91x03
xa4ndimx01xa5dtypexa4int8xa4dataxc7x03x00x02x01x00
xa8compressxc0xaacategoriesx86xa3typxa5indexxa5klass
xa5Indexxa4namexc0xa5dtypexa6objectxa4datax93xa4blue
xa5greenxa3redxa8compressxc0xa7orderedxc2xa8compress
xc0xa5shapex91x03xa5dtypexa8categoryxa5klass
xb0CategoricalBlockxa8compressxc0'
Metadata and Stats
Pandas gives a lot of information about data frames. Check out these methods:
>>> df.index
Index(['I', 'II', 'III'], dtype='object')
>>> df.columns
Index(['a', 'b', 'c', 'd'], dtype='object')
>>> df.describe()
a b
count 3.0 3.0
mean 2.0 5.0
std 1.0 1.0
min 1.0 4.0
25% 1.5 4.5
50% 2.0 5.0
75% 2.5 5.5
max 3.0 6.
Selecting Data
Data frames let you select data. If you want to select a row by index, you need to use the loc attribute. To select columns, you simply use the column name. Here is how to select individual rows, individual columns, a slice of rows, a slice of columns, and last but not least, a rectangular section (subset of rows and subset of columns from these rows):
Single row
----------
>>> df.loc['II']
a 2
b 5
c 2017-09-02 00:00:00
d green
Multiple rows using integer index (no 'loc')
--------------------------------------------
>>> df[:2]
a b c d
I 1 4 2017-09-02 red
II 2 5 2017-09-02 green
Single column
-------------
>>> df['b']
I 4
II 5
III 6
Multiple columns
----------------
>>> df.loc[:, 'b':'c']
b c
I 4 2017-09-02
II 5 2017-09-02
III 6 2017-09-02
Rectangular section
-------------------
>>> df.loc[:'II', 'b':'c']
b c
I 4 2017-09-02
II 5 2017-09-02
Using integer index (when actual index is not integer)
------------------------------------------------------
>>> df.iloc[:2, 1:3]
b c
I 4 2017-09-02
II 5 2017-09-02
In addition to those direct addressing data selections, you can also select based on values. For example, you can select only rows with even values in column b:
>>> df[df.b % 2 == 0]
a b c d
I 1 4 2017-09-02 red
III 3 6 2017-09-02 blue
Sorting Data
Pandas gives you sorting too. Let’s sort the following data frame by index (rows) and by column. Multiple-level indexing is supported too:
index=['one', 'two', 'three', 'four', 'five']
df = pd.DataFrame(np.random.randn(5,2),
index=index,
columns=['a','b'])
Sort by index (alphabetically and descending)
---------------------------------------------
>>> df.sort_index(ascending=False)
a b
two -0.689523 1.411403
three 0.332707 0.307561
one -0.042172 0.374922
four 0.426519 -0.425181
five -0.161095 -0.849932
Sort by column
--------------
>>> df.sort_values(by='a')
a b
two -0.689523 1.411403
five -0.161095 -0.849932
one -0.042172 0.374922
three 0.332707 0.307561
four 0.426519 -0.425181
Conclusion
In this part of the tutorial, we covered the basic data types of Pandas: the series and the data frame. We imported and exported data, selected subsets of data, worked with metadata, and sorted the data. In part two, we’ll continue our journey and deal with missing data, data manipulation, data merging, data grouping, time series, and plotting. Stay tuned.
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